面向写密集与读密集振荡工作负载的自适应热点感知索引
An Adaptive Hotspot-Aware Index for Oscillating Write-Heavy and Read-Heavy Workloads
AI总结：
针对 HTAP 中读写负载振荡导致单一索引性能不稳的问题，提出双向自适应的 AHA-tree，聚焦热点区域优化，使写性能接近 LSM-tree、读性能可匹配 B+-tree。
摘要：
HTAP 系统旨在处理事务型和分析型工作负载。除了在任意给定时刻存在混合工作负载之外，工作负载还会随时间变化。一种常见的持续变化工作负载会在某些时段偏写密集、其他时段偏读密集之间振荡。振荡工作负载可见于许多应用中。诸如 B+-tree 和 LSM-tree 之类的索引无法始终保持同等优异的性能。传统自适应索引并未解决这一问题，因为它们只关注沿一个方向进行自适应。本文研究如何利用自适应索引支持振荡工作负载，使底层索引结构能够沿两个方向自适应。基于真实世界数据集存在倾斜这一观察，研究重点是在热点区域内优化索引。文中提出了 Adaptive Hotspot-Aware Tree（简称 AHA-tree），其自适应是双向的。实验评估表明，在写密集的事务型工作负载下，AHA-tree 能够表现出与 LSM-tree 相当的竞争力。当切换到读密集的分析型工作负载后，AHA-tree 能够逐步自适应并表现出竞争力，并且在读性能上可与 B+-tree 匹敌。
英文摘要：
HTAP systems are designed to handle transactional and analytical workloads. Besides a mixed workload at any given time, the workload can also change over time. A popular type of continuously changing workload is one that oscillates between being write-heavy at times and being read-heavy at other times. Oscillating workloads can be observed in many applications. Indexes, e.g., the B+-tree and the LSM-tree, cannot perform equally well all the time. Conventional adaptive indexing does not solve this issue as it focuses on adapting in one direction. This paper studies how to support oscillating workloads with adaptive indexes that adapt the underlying index structures in both directions. With the observation that real-world datasets are skewed, the focus is to optimize the index within the hotspot regions. The Adaptive Hotspot-Aware Tree (or AHA-tree, for short) is introduced, where its adaptation is bi-directional. Experimental evaluation show that AHA-tree can behave competitively as compared to an LSM-tree for write-heavy transactional workloads. Upon switching to a read-heavy analytical workload, AHA-tree can gradually adapt and behave competitively, and can match the B+-tree in read performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
